Indian IT Firms Embrace AI Amid Mixed Q4 Results and Shifting Industry Dynamics

Indian IT Loves AI Washing | AIM Media House

Indian IT seems to be hopping onto the generative AI bandwagon lately. Amid weak Q4 results, leading players like TCS, Infosys, HCLTech, and Wipro have been quick to claim that AI is now integral to every client conversation and all major deals. Despite these bold claims, they seem to be wary of directly disclosing GenAI revenue from any deals. This raises questions over whether these companies are merely 'AI washing' or creating actual value for the clients. 'AI washing' refers to vague and overstated claims around AI adoption, lacking measurable outcomes. The Q4 earnings have thrown this into sharp focus. In contrast to Indian IT, global IT giant Accenture, which operates in a similar market, revealed that its generative AI bookings reached $1.4 billion in Q2 FY25, up from $1.2 billion in the previous quarter. This, despite the company reporting a 5.8% QoQ decline in revenue, while reporting a 5.4% increase YoY. Generative AI comprised 6.7% of Accenture's total order bookings of $20.9 billion. Among the Indian tech giants, TCS reported $7.4 billion in revenue in Q4 FY25, a 0.8% sequential rise and 5.3% YoY growth. The company announced record-high deal wins of $12.2 billion, up from $10.2 billion in the previous quarter. CEO and MD K Krithivasan claimed that AI was now part of every deal. Expertise in AI and digital innovation enabled TCS to invest in an agent-based AI form, with over 150 solutions across various verticals for its clients. Despite this, the company did not disclose direct revenue from GenAI. This is a departure from Q1FY25, when the company reported a generative AI pipeline of $1.5 billion, suggesting early traction that had since failed to convert into topline growth. Infosys, meanwhile, posted $4.7 billion in Q4 revenue, down 4.2% QoQ in USD terms. CEO Salil Parekh demonstrated enthusiasm for generative AI despite the weak quarter. He said the company is leading in AI agents, similar to TCS, and working on over 400 generative AI projects. That said, despite a ramp-up from just 100 projects in the previous quarter, Infosys also declined to give any AI-related revenue figures. In the last quarter, Parekh distanced Infosys from 'AI washing' claims. Infosys closed FY25 with a negative 4.2% revenue growth in USD QoQ at $4.7 billion. Its peer, Wipro, echoed a similar stance. CEO Srini Pallia said all 17 deals signed in Q4, worth $1.8 billion, included generative AI components. For the full year, the company reported that it closed 63 large deals, totalling $5.4 billion, representing 17.5% year-over-year growth. Wipro has forecast a decline of up to 3.5% in Q1 FY26, signalling more headwinds despite its AI narrative. Yet, like its peers, Wipro has kept away from directly quantifying revenue tied to AI. When asked about the cannibalisation of deals due to generative AI, Pallia, during the company's earnings call earlier this month, stated that Wipro is now incorporating generative AI into all its solutions. "GenAI was not part of the earlier deals, but in all the new deals, we're going to infuse it," Pallia said. For the year ending March 31, Wipro reported gross revenue of $10.4 billion, reflecting a 0.7% decline year-over-year. However, net income for the year rose by 19% to $1.5 billion. Looking ahead, Wipro further anticipates Q1 FY26 revenue to fall between $2.505 million and $2.557 million, indicating a projected decline of 1.5% to 3.5%. HCL, with a market capitalisation of roughly a third of TCS' and two-thirds that of Infosys, announced 12 generative AI-focused deals and highlighted its 500 GenAI engagements across 400 clients through its AI Labs. The company's Q4 revenue stood at $3.4 billion, down 1% QoQ, and deal bookings were at $3 billion, taking FY25 total to $9.26 billion -- a 5% drop from the previous year. HCLTech focused on exclusive AI and generative AI deals, securing 12 new agreements, including those involving agentic AI and automation processes, for the quarter. This announcement differs from other IT firms that shied away from mentioning AI-specific deals. CEO C Vijaykumar maintained a longer-term perspective. He noted that the company's AI investments will yield returns over time. "Don't worry about the investments now -- look at the ROIs we can get on the back of it," he said. Also, Tech Mahindra, the firm behind the creator of Project Indus, India's first Indic LLM, posted flat revenue growth for the quarter. However, it recorded a 77% rise in profit. Speaking on the company's generative AI strategy, Mohit Joshi, CEO and MD, shared that the company is formally unveiling its "AI delivered right" approach. "AI is now woven into the fabric of our data and engineering service lines," he said. "It's not siloed anymore. The vast majority of our clients are already leveraging some form of our AI offerings." Similar to the others, with revenue growth faltering, the integration of AI in the deals did not add any immediate value. Meanwhile, LTIMindtree did not disclose any information about AI, apart from its past partnerships. CEO and MD Debashis Chatterjee said that the revenue was driven by a significant array of AI-led deal wins. "[It] illustrates the pervasive integration of AI across our service offerings," he said. Surprisingly, LTIMindtree reported significantly better revenue compared to its larger peers in Indian IT. The company's revenue stood at ₹9,771.7 crore for the quarter ended March 31, reflecting a 1.1% sequential growth and a 9.9% rise year-on-year. Speaking at an industry event in Mumbai in February, HCLTech's Vijayakumar pointed out that it was time for Indian IT to rethink its 30-year-old business model or risk becoming obsolete. AI was seen to steer the path ahead for them to remain relevant in the race. Infosys' Parekh echoed this "paranoia" and called for a non-complacent approach. Now, generative AI is expected to accelerate software development by automating coding and reducing project timelines. Last week, Zoho co-founder Sridhar Vembu raised red flags about the state of India's IT services industry. He responded to a comment on the dismal Q4 earnings from top IT firms, calling it a "total washout". He pointed out that there are deep-rooted inefficiencies and structural flaws that go beyond short-term market cycles or AI. Vembu also noted that Indian IT's 30-year-old business model is at a pivotal juncture. "We have to challenge our assumptions and do fresh thinking." Meanwhile, CP Gurnani, former CEO of TechMahindra, believes that this is recoverable. "The pain is temporary," he wrote in a LinkedIn post. Taking the example of COVID, Gurnani said that certainly there is an inflexion point, but "Indian IT giants have constantly moved up the value chain all through the course of their storied existence." This quarter is supposed to be a wake-up call for Indian IT following the Accenture results, but clearly AI has taken the spotlight with a long shadow over the firms.

Mid-Sized IT Firms Outperform the Big-Four in Q4 FY25 | AIM

For years, the Indian IT story has centred around the dominance of the "Big Four" -- TCS, Infosys, Wipro, and HCLTech. But that's changing rapidly. In FY25, it's the mid-sized companies -- Persistent Systems, Mphasis, LTIMindtree, L&T Technology Services, WNS, and even the slightly larger Tech Mahindra -- that have emerged as the real growth engines of the industry. Persistent Systems, for instance, posted a staggering 25% year-on-year revenue growth for Q4 FY25, a figure that starkly contrasts with the Big Four's lacklustre single-digit or negative growth outlook. HCLTech barely managed to achieve a 1.2% revenue growth, while TCS and Wipro crawled to a 0.8% increase. Infosys, on the other hand, experienced a revenue dip of 4.2%. These four giants also project a very bleak outlook for the next year, which contrasts sharply with that of the smaller IT firms. Persistent clocked its 20th consecutive quarter of growth, something even the giants have struggled to maintain lately. CEO Sandeep Kalra pointed out that operational discipline and client trust were key. Behind the scenes, founder Anand Deshpande made it clear that Persistent's early bet on agentic AI, which many mid-sized IT firms have started in the last few quarters, is the reason behind this. While Persistent led the pack, LTIMindtree also delivered a positive performance, albeit a more modest one. The company reported a 9.9% year-over-year increase in revenue and a 2.6% rise in net profit. Despite a slight dip in dollar revenue, the firm's strong order book, again driven by AI-powered deals, helped it outperform a sluggish broader market. Debashis Chatterjee, LTIMindtree's CEO, credited the wide integration of AI across services for the resilience, a sentiment increasingly echoed across mid-tier IT firms. And then came Mphasis, pulling off its strongest sequential growth in three years. Revenue increased 2.6% sequentially in USD terms and 2.9% in constant currency, a remarkable achievement considering the macroeconomic pressures that have battered the broader sector. What's even more telling is that 59% of Mphasis' deal wins were AI-led. CEO Nitin Rakesh made it clear: tech and AI are now at the very core of their strategy. Banking, financial services, insurance, and TMT verticals led the charge, compensating for weaknesses in other areas, such as logistics. Meanwhile, L&T Technology Services (LTTS) achieved a stellar 17.5% year-over-year revenue growth in rupee terms. Profits dipped 9%, but operational metrics remained strong, and the outlook for FY26 is bullish. Amit Chadha, the CEO, was confident that the coming year would be even better, backed by a strong pipeline of large digital and AI-led deals. Automation and AI were front and centre in their narrative too, hinting at a clear industry pivot. WNS, the smallest firm among them, reported a slightly lower quarterly revenue of $336 million, showcasing flat growth compared to the same period last year and an increase of only $3 million from the $333 million reported in the previous quarter. Despite this, the company projects a 7-11% growth in the next quarter. Despite the weak quarter, CFO Arijit Sen stated that the positive outlook is based on current visibility levels and includes a 2% revenue contribution from Kipi.ai, with 90% visibility already achieved to the midpoint of revenue guidance. Even Tech Mahindra, which experienced a rough run in recent quarters, managed to show signs of life. While revenue remained relatively flat, the company reported a massive 77% increase in net profit. The flat revenue was attributed to delays in renewals with major clients, particularly in the high-tech sector. But the fact that Tech Mahindra's bottom line improved despite that speaks volumes about its renewed operational focus and margin discipline. This shift isn't coincidental; it's part of a larger trend. Unlike the larger firms, which are still engaging in AI washing and slowly integrating AI into their deals with a 30-year-old business model, smaller firms have a slight advantage due to their high agility. When mid-sized Indian IT firms began announcing acquisitions and partnerships related to agentic AI over the last three quarters, it felt like just another attempt to keep pace with the generative AI wave. But as Q4 results rolled in, it became clear that these early bets are already paying off. A year ago, the mood among mid-sized Indian IT firms was clear. Instead of building expensive generative AI models from scratch, they would partner with or acquire specialised startups to develop use cases. LTIMindtree, for instance, announced a $6 million investment in Voicing AI, a US-based startup that builds human-like AI voice agents capable of operating in over 20 languages. LTIMindtree also partnered with GitHub to integrate Copilot into its developer training programs, preparing its workforce for an AI-first future. Similarly, Mphasis doubled down on conversational AI with the launch of NeoCrux, a tool designed to enhance developer productivity through AI orchestration. In October 2023, the company also acquired Silverline, a Salesforce consulting partner, to bolster its customer experience and conversational AI capabilities. Persistent Systems, on the other hand, focused on the foundational issues of AI adoption -- namely, privacy and governance -- with its acquisition of Arrka, a Pune-based data privacy consultancy. While major players such as TCS, Infosys, and HCLTech developed internal capabilities and launched AI business units like AI.Cloud, Topaz, or AI Force, mid-sized IT firms like LTIMindtree and Mphasis chose to focus on agility. "We would want to leverage startups for technologies that are coming up, rather than building expensive in-house models that may become obsolete quickly," said Nachiket Deshpande, president of AI services at LTIMindtree, in an earlier conversation with AIM. Last year, companies like Happiest Minds, Hexaware, Quest Global, Coforge, Sonata, and GlobalLogic had already accelerated their acquisition of AI capabilities. However, the conversation has now shifted squarely to agentic and generative AI, as firms realise that enabling autonomous workflows is the next significant differentiator.

India ready to recreate UPI magic with AI

"We need to, at some point, think of developing a full AI stack as a DPI. I think then we can see a real proliferation of AI to the end users in India," said Rajendra Kumar, secretary (border management) at the Ministry of Home Affairs (MHA).The time is ripe for India's "UPI moment" in artificial intelligence, and a comprehensive AI stack built as a digital public infrastructure (DPI) will help take AI to the masses, industry leaders and policymakers said. Speaking at the third edition of The Economic Times Digital Transformation Dialogues in New Delhi, experts sought regulations that would ensure trust and accountability without stifling innovation. While concerns on job displacement remained, education and entrepreneurship could be a solution to combat this, they said in a conversation moderated by ET's Dia Rekhi. "We need to, at some point, think of developing a full AI stack as a DPI. I think then we can see a real proliferation of AI to the end users in India," said Rajendra Kumar, secretary (border management) at the Ministry of Home Affairs (MHA). He noted that such a model would empower both startups and government departments to develop use cases and launch applications faster, without the burden of building their own compute or training their own models. Sunil Gupta, cofounder and CEO of Yotta Infrastructure, agreed: "We are all waiting for the UPI (unified payments interface) moment to happen in AI now. All the enabling factors in India are there." He pointed out that India has low-cost and abundant graphics processing units (GPU) infrastructure for startups and researchers for model training and to trigger an "inferencing wave". Inference is the process of an AI model making a prediction or solving a task based on data it was trained on. "AI use cases, if you are able to bring to Indian masses at no or very low value - that is how we consume as Indians - it is going to proliferate at population scale," Gupta said. Such a time is possibly "coming very fast" as AI initiatives in areas like healthcare, agriculture and education are already underway, he added. Era of agentic AI Among the most talked-about trends, according to the panellists, is the emergence of agentic AI - systems capable of making autonomous decisions. "The hype (about agentic AI) is absolutely real and it is getting better and better every day," said Sanket Atal, managing director of Salesforce India operations. "Today, we do more than a trillion transactions - whether it's predictable or generative-every week around the world. With that came copilots, etc. And today, we are in the era of agentic. Agentic just ties everything together." Atal emphasised that the core of AI remains data, which is why Salesforce is investing in tools like its Data Cloud, which enables users to bring all their data together. "That establishes an awesome foundation to create agents and have everybody leveraging AI," he said. Besides enterprises like Salesforce, many startups, too, are betting on agents. Ankush Sabharwal, founder and CEO of Corover AI, said developers using its BharatGPT platform have already created around 3,500 AI agents. However, he cautioned against adopting AI agents blindly: "We should not have FOMO (fear of missing out) of using and force-feeding the new technology. Currently, the AI agent cost is more than the actual physical agent." While more than 1,000 users have shown initial interest, Corover AI has only about 100 managed accounts. "They're not paying because there is no immediate value," Sabharwal said. The panel also debated whether India should build AI capabilities from scratch or on top of existing applications. Sachin Bhatia, cofounder of cloud communication platform Exotel, said India should focus on building companies that enable AI. "I would love an Nvidia to happen out of India - now, that would be of value," he said. "Ultimately, the winners are the people who own the infrastructure, or who own the application - everything in the middle is almost free now." To regulate on not Regulation of AI was a key theme, with consensus leaning toward 'light-touch' frameworks. Sabharwal argued that lack of regulation has hampered adoption. "People are not able to put millions of dollars in somewhere where policies are not there," he said. Ashish Aggarwal, vice president and head of public policy at Nasscom, underscored the need for clear regulatory intent. He advocated looking at achieving desired objectives as well as ethical AI practices considering the entire value chain across AI developers, deployers, clients and end customers. "Responsible AI is driven by the very real business need to get things right, and also to avoid any liability," Aggarwal said. "One of the things that we are now working with the industry on is to say that once you put out something, then how can you effectively demonstrate that you are living those values and living those governance principles?" Bhatia of Exotel offered a simple principle for companies to stand by - never try to fake that an AI agent is a human being. "If it's AI, it should be known that it's an AI," he said. "The second thing is, building observability within any use case, which means that what the AI did should be transparent - you should have a log of what happened so that you can trace it back," Bhatia said, Atal highlighted how Salesforce builds the concept of trust in their AI products. There are guardrails to contain hallucinations and toxicity in large language models (LLMs). "We enable the users of our technology to actually keep their own data private and not have that be trained in the LLM," he said. Impact on jobs As AI gets more sophisticated, it's bound to impact more and more jobs, panellists agreed. "There are not going to be that many jobs. That is very clear," Exotel's Bhatia said. "I want us to focus on that problem from a regulatory or a government (perspective). For example, if you are making people redundant, how do you make sure that their wellbeing is taken care of over some time?" While new roles will emerge, he warned they won't fully offset the old ones. Nasscom's Aggarwal pointed to education reform and fostering an entrepreneurial mindset as long-term solutions. "What is happening is some tasks are getting automated... And given the global need for stuff and India being the supplier of services, we don't have a demand side constraint. In that sense, the short and medium-term story is actually good for India," he said. "In the long term, we don't know what is there, but it's a topic worth really thinking about." Despite the challenges, the panellists remain optimistic. "Huge gains and amazing economic impact can be had by adopting AI," Salesforce's Atal said. "I don't think everybody needs to become an AI expert. The whole concept of being able to design systems is to enable people to use the technology for their own betterment." Dilip Kant Jha, chief information officer of SLMG Beverages, the largest Coca-Cola bottling company in India and South Asia, concurred. He said his firm has started using agentic AI to streamline logistics, predict safety incidents, optimise inventory and prevent overproduction. "It gives you real-time visibility - looking at the market trends, it will tell you what to produce, what to send, what stocks you should keep - it is a gamechanger," Jha said. However, there should always be a human in the loop and a focus on ensuring datasets are not biased, he added. Privacy safeguards are vital in AI deployment but exceptions may need to be made in cases where the technology is used to strengthen national security interests, MHA secretary Kumar said. He stressed the need for carve-outs for exceptional cases "where we need to really ensure that the threats are detected real-time and then we can respond to them as quickly as possible." AI tools can enable more efficient border management and help anticipate and respond to breaches more proactively, said Kumar, who was earlier additional secretary at the electronics and IT ministry. On the question of AI regulation, he said basic frameworks should be put in place to address issues of privacy, data handling, and fairness. "We can put in place a light-touch regulation, in the sense of ensuring that we do not restrict full innovations by putting in place too many expectations that they (firms) need to adhere to from day one," Kumar said. As India races toward an AI-powered future, getting the infrastructure, regulation, and trust equation right will be key to take it to all. (With inputs from Annapurna Roy)

What India's Early Stage AI Investors Want: Depth In Data, Talent And Product

The evolving due diligence models of VCs in the AI space -- and even those outside -- has reset expectations from early stage founders and startups What is the state of early stage AI investments in India and are investors changing the way they evaluate startups and founders amid raging debates on sovereign models, Indian LLMs as a key focus area, beyond GenAI applications and SaaS tools? More and more investors are factoring in the influence of GenAI and its future potential impact on sectors and industries when evaluating early stage deals. Even two years into the AI revolution, Indian venture capital in AI is concentrated towards startups in the applications and developer tools layer. Murmurs of sovereign models and made-in-India large language models might yet influence the course of VC dollars flowing into AI this year, but it's still too early to call this a significant shift. However, the evolving due diligence models of VCs in the AI space -- and even those outside -- has reset expectations from early stage founders and startups. And this includes the potential disruption from sovereign models and Indian LLMs in the future. Early stage fund managers and VCs are digging deeper into the architecture of the AI stack, dependency on APIs, the flexibility and efficiency of AI models, cost overruns and revenue leaks, and whether AI is core to the startup's product roadmap. While most early stage investments did not carry the full due diligence weight in the past, this is changing, with technical due diligence becoming very critical. At the same time, investors are reevaluating how they see the team behind the idea or the product, given the need to be highly conversant with the bleeding edge of AI development. Are teams ready for the next big jump, fund managers are asking as they look beyond the founder. Inflection Point Ventures' Ankur Mittal told Inc42, "We now evaluate data strategy, model flexibility, and AI-driven defensibility. Startups that capitalise on proprietary data and thoroughly integrate AI into their value offering outperform those who only use AI as an add-on." Instead of focussing on whether they have built their AI models from scratch or integrated third-party solutions, the real criterion should be the impact of technology on end users. "The first question should always be: What problem does it solve, and how does it enhance customer experience? If a task that once took hours can be completed in minutes, that is a game changer," Kushal Bhagia of All In Capital told us in an interview last month after the firm launched its second early stage fund. Once the user impact is established, the focus shifts to technical depth and execution. Companies that create highly personalised AI experiences for specific user segments tend to develop more substantial products. The All In Capital founder cited the example of an AI-powered skilling startup SuperNova, which enables users to speak English through structured interactions, rather than an open-ended course. This directly helps professionals like hotel employees to upskill for their current job, and it's a more compelling learning experience than Duolingo or something like that. In a similar vein, investors think that AI that can redefine existing experiences will win in the long run, since this is the intuitive path set by the internet age companies. While AI-driven SaaS solutions for niche B2B applications are gaining traction in sectors like healthcare, fintech and edtech, consumer-facing AI products in India are still nascent. Where The Money Is Flowing Marquee VC firms such as Prosus, Tiger Global, Peak XV Partners, Accel, Lightspeed among others have already made several early stage SaaS bets in the GenAI space -- across the applications layer as well as the developer tools layer. Many of these larger funds foresee startups in their portfolio to bag major returns as the AI wave leads to consolidation, but the point is to survive till that stage without needing to constantly evaluate your go-to-market strategy or your product-market fit. But this is the reality in SaaS and the applications layer. For instance, in the SaaS space, most investors Inc42 spoke to believe things are changing at such a high pace that it's hard for even practiced founders to keep up. The revenue run rates are growing rapidly across industry and SaaS companies are reaching the $1 Mn ARR holy grail faster than ever. This revenue spike has created something akin to blinders for founders, so it's easy to get swept up in this frenzy and not build a real moat. AI is no longer a moat, it is par for the course in SaaS. So the real product-market fit comes from elsewhere, and many investors believe this is why SaaS is dead, and AI is the new software. This despite the fact that a majority of the venture capital invested in the past two years has gone for AI adoption in some ways or the other, benefitting the SaaS ecosystem. AI has become a central pillar for efficiency and startups have looked to adopt GenAI at scale rather than hiring en masse to remove operational inefficiencies. As a result, SaaS-AI hybrid tools have boomed. For investors like Abhishek Prasad, managing partner at Cornerstone Ventures, the AI wave in SaaS is prompting fundamental changes in deal evaluation. His lens has shifted from traditional SaaS metrics like CAC and ARR growth alone to newer AI-driven qualifiers. "With AI becoming core to SaaS, we are interested in how AI is being leveraged by these companies, what is the impact it is making to the value proposition, is it creating new moats, and is the cost of leveraging AI capabilities delivering the right ROI to both the startups and their customers." Inc42 reported that AI startups comprised nearly 40% of new SaaS ventures funded in 2024, a sharp leap from 19% in 2022. In total, Indian SaaS startups raised over $2.1 Bn in 2024, up 31% YoY. A growing chunk of this capital is flowing toward companies that are not just building on AI, but being built by AI. Leading VC firms are targeting startups creating AI-based consumer and business applications and developer tools. They believe such ventures have the potential to launch globally competitive solutions.n"We have not seen many AI startups targeting direct-to-consumer experiences in India yet, but it's only a matter of time," said Bhagia. Unless new consumer products completely reimagine the user experience, they may have little competitive advantage over incumbents. Plugging The Foundational Gaps Global tech giants dominate the infrastructure, cloud and foundational model layers. Competing in these areas would require one to build advanced technology and make massive investments over a period, as we explained in our recent look at why India's deeptech future is so precariously poised. India's comparative advantage, as has been made clear over the past two years, lies in the developer tools and applications layer. These segments align well with the depth of talent in India, and is linked to its SaaS and IT services prowess. There are of course exceptions such as Sarvam and Bhavish Aggarwal's Krutrim, which are taking their own routes to build large and small models for Indian languages. Even as India increases its focus on artificial intelligence (AI), the country accounts for just 3% of early stage AI infrastructure and foundational model startups. AI application-focused startups in India are on the rise, capturing a significant 65% market share, as per Sense AI's Annual Ventures 2025 report. The report claims AI tooling accounts for 22% of all funded AI startups in India. "India has a large pool of software developers with AI knowledge. Instead of building infrastructure, they rely on global providers like Google, Microsoft, and AWS, which offer free cloud access to startups in their early years," said Rahul Agarwalla, founding partner, SenseAI Ventures. DeepSeek Vs Early Stage Indian AI Startups Since the entry of DeepSeek in 2025, the race for faster AI adoption and implementation has intensified. As a result, companies have been focusing heavily on LLM and model investments. DeepSeek was built at a cost of around $5.5 Mn as compared to OpenAI, which invested over $100 Mn to develop its large language model GPT-4. This has fuelled a new race for a sovereign model for India and Indic language models. "The number of startups attempting to build LLMs in India is growing, especially after DeepSeek's success. The recognition that building an LLM doesn't require a billion dollars has fueled this. Instead, startups can do it with much less capital, like around $6 Mn. The number of companies in this space is expected to rise, potentially reaching 20, 30, or even 40 by the end of the year," said Agarwalla. From the geopolitical shifts of the past six months, DeepSeek's disruption to Silicon Valley's AI giants and the very real possibility of a fragmented AI world, many in the Indian startup ecosystem are finally speaking about sovereign models for the first time. "If you had asked me just six months ago, I would have said there's no need for India to develop a 'sovereign model', but a lot has changed in that time. And now I definitely believe that we need to take control of our destiny," Ashwin Raguraman, founder of Bharat Innovation Fund, said. Now Vertical LLMs and models are becoming all the rage, and the hope is that these could one day become part of a full-blown India-made LLM. Investors are today seeking founders that are capable of creating solutions for industries like manufacturing, healthcare and legal with data as a moat. These sectors are seen to be traditionally slower in adopting SaaS or other technology, but the hope or rather the vision is that AI can bring in measurable outcomes in terms of cost efficiencies to be worth the investment. Investors believe domain-specific agentic AI models for finance, hospitality, banking hold greater potential for monetisation and value creation. The Talent Piece Of The Puzzle The AI adoption wave is also influencing how investors assess the founding team, another key facet of the early stage investor evaluation kit. Technical depth is perhaps the most essential and paramount element, which bodes well for founders with the right engineering and AI experience, but also complicates the view for investors who are backing a second-time founder now building in AI after starting up again. In these cases, investors are looking for domain fluency and the ability to iterate quickly when it comes to products or changes in the AI landscape. The ability of the AI product to extract the best ROI for its eventual customer and figuring this out is often critical besides the technical depth. Inflection Point Ventures' Ankur Mittal revealed, "We seek entrepreneurs who have not only created AI models but also effectively implemented them at scale, resolving real-world challenges with demonstrable results." Besides this, a good AI team consists of more than simply technical talent; it also comprises deep domain knowledge, research-backed capabilities and an understanding of unit economics fundamentals. It's also about having a team that can sustain itself when there are major disruptions such as DeepSeek. Building For The Long Haul If we read between the lines of what VCs and investors are saying about early stage AI deals, the message is that one needs to think long term and not just eye short term gains within the AI hype cycle. The rapid rise in investments into Indian AI applications and developer tools could become a big burden on the investor ecosystem if these products do not mature in the long run or deliver the results. Ultimately, there is a risk of some of these investments turning out to be FOMO-induced duds. But at the same time, there is a growing realisation that India can also go beyond this stage. The fight for space in the global AI race has forced Indian companies on the LLM trail and behind foundational models. This will have deeper ramifications on AI applications as well without a formidable product vision and heavy reliance on ARR projections. As we also noted in our recent deep dive into India's deeptech woes, VCs and investors are sharpening their knives and looking for genuine technical depth in companies, defensible data moats, and teams that can actually navigate the breakneck speed of AI.